U.S. Pat. No. 6,738,748 to Wetzer and assigned to Accenture LLP relates to performing predictive maintenance on equipment. Wetzer discloses a data processing system and method to predict maintenance based upon one or more estimated parameters such as longevity, probability of failure (mean time between failure), and financial estimates.
United States Patent Application 2004/0148136 to Sasaki et al assigned to Toshiba Kikai Kabushiki Kaisha relates to a system for predictable maintenance of injection molding equipment. Sasaki discloses a data processing system and method for monitoring injection molding equipment where operational data is compared to theoretical estimated expected life data. For example, the hours of use may be compared to an expected life limit or, the maximum frequency of use may be compared to an expected life limit.
U.S. Pat. No. 6,175,934 to Hershey et al assigned to the General Electric Company relates to a satellite based remote monitoring system. The system places remote equipment into a test mode to perform remote predictive assessment. A disadvantage of this approach is the requirement to take a piece of equipment off-line to conduct the test.
U.S. Pat. No. 6,643,801 to Jammu et al and assigned to the General Electric Company relates to a method for analyzing fault log data and repair data to estimate time before a machine disabling failure occurs. Fault data and repair data are used to estimate the time before a failure occurs. Service information, performance information, and compartment failure information are analyzed to determine a performance deterioration rate to simulate a distribution of future service events. The system is based upon operational levels of vibration in contrast to ideal or acceptable levels of vibration.
U.S. Pat. No. 6,192,325 to Piety et al and assigned to the CSI Technology Company and relates to a method and apparatus for establishing a predictive maintenance database.
U.S. Pat. No. 6,799,154 to Aragones et al assigned to the General Electric Company relates to a system for predicting the timing of future service events of a product.
However, problems remain with the known prior art approaches that apply estimated or theoretical values to predictive maintenance. A component or part may fail in advance of the estimated values and there is no warning or indication that a component or part may fail in advance of the estimate values. A component or part may be replaced when it still has a good useful life. Any of these situations cause unnecessary expense and maintenance.
For example, the estimated useful life of an oil filter in the hydraulic circuit of a power pack might be 10,000 hours of operation. The prior art systems simply record the number of hours of usage, and then schedule a replacement of the oil filter when the hours of usage approach or reach the limit of 10,000 hours. However, if a seal fails or contaminants enter the oil system, the oil filer could fail in advance of reaching the limit, potentially causing damage to other components in the hydraulic system and power pack.
In addition, the prior art systems do not take into account different environmental aspects of operating equipment at different customer locations and different global locations around the world. For example, humidity, air temperature, cooling water quality, and altitude may impact the performance and reliability of a molding system. For example, some customers run equipment harder than other customers. The prior art systems do not take into account the aspect of supporting and maintaining such equipment on a global scale.
The prior art approaches relate to predictive maintenance. Predictive maintenance attempts to maximize the use of a component or part based upon statistical predetermined information in advance of a theoretical point of failure. However, predictive maintenance does not take into account events or indicators that warn of a premature failure in advance of the theoretical point of failure.